Analysis Of Micrographs Research Articles

Evaluation by Scanning Electron Microscopy and Energy Dispersive X-Ray Spectroscopy of the Effects of Root Canal Instrumentation on the Radicular Dentine.

One area of technological advancement has been the shift from stainless steel hand tools to nickel-titanium (Ni-Ti) rotary tools. This paper aims to perform an invitro comparative study to evaluate the efficacy of five endodontic manual and rotary instruments such as Kerr files, Orodeka Plex V, ProTaper Flydent NiTi super files, and ProTaper Flydent NiTi super files in combination with an ultrasonic endodontic E3D Diamantata EMS scaler used for root canal shaping. The following aspects were highlighted: effective removal of smear layer (SL) from the dentinal tubules in the coronal 1/3, middle 1/3, and apical 1/3 of the root canal, appearance of cracks in the dentinal walls by SEM analysis, and highlighting of dentin mineral content and remnant debris by EDX analysis. In the study, 48 monoradicular, uninjured teeth were taken and divided into four groups, each with 12 teeth to be canal modeled, then longitudinally sectioned; the active surface was metallized for SEM-EDX analysis. From the SEM micrograph analysis, the morphology of the crystallites, the distribution of SL components, and the highlighting of instrumentation path traces/amplitudes were observed. By analyzing the SEM photomicrographs at 750 and 800× magnification, it was possible to highlight the cracks in the root dentinal walls. Thus, in comparison with Lots I and II which did not present cracks, in Lot III multiple cracks of the root dentinal walls were identified, especially in the coronal 1/3, and in Lot IV cracks were present with preponderance in the coronal and apical 1/3 of the root canals. The EDX images with atom mapping show very well the rather homogeneous elemental distribution for Ca, N, and P, respectively inhomogeneous for Na, Ca, Si, C, N, O, and P elements, as a result of the presence of remnant products from the processing of the root dentinal walls. Root canal instrumentation performed with both manual Kerr needle instrumentation by a convection technique and the rotary, Orodeka system, by crown-down technique, resulted in minimal SL formation in all three root canal areas, unlike the ProTaper FlyDent system. Most cracks were recorded in the case of the ProTaper Flydent rotary instruments and the one in which it was paired with a diamond ultrasonic scaler. The occurrence of dentinal cracks causes a decrease in dentinal microhardness, favoring the occurrence of root fractures.

Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model

Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results.

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of measuring the diameter of more than 1,800,000 HDL particles with the deep learning model YOLOv7 (you only look once) from micrographs of 183 HDL samples, including patients with dementia or normal cognition (controls). This method was shown to be more efficient and accurate than conventional image analysis software. Using this method, we found a higher abundance of small HDLs in participants with dementia compared to controls in patients with the apolipoprotein E (APOE) ε3ε4 genotype, whereas patients with the APOE ε3ε3 genotype had higher variability in the abundance of different HDL subclasses. Our results show an example of accurate individual HDL particle diameter measurement for large-scale clinical samples, which can be expanded to characterize the relationship between disease risk and other nanoparticles in the sub-20-nm diameter size range.

Sonochemical synthesis of CQDs from coffee husk: Insights in aggregation mechanism, optimization and sustainability analysis.

A green methodology for the synthesis of carbon quantum dots (CQDs) from coffee husk without the use of any toxic solvents is proposed in this work. Sonochemical exfoliation of biochar, obtained from the thermal carbonization of coffee husk (from a certified coffee seeds) at low temperature in an air-restricted atmosphere, is described as an alternative procedure for the sustainable production of CQDs. The synthesized CQDs exhibited blue fluorescence with a strong maximum emission band at 410nm when excited at a maximum absorption wavelength of 330nm. The obtained CQDs were evaluated by various physicochemical methods. According to the results, TEM investigation indicates the average size of CQDs is 8.27±1.57nm with a quasi-spherical morphology. The aim of this study is to optimize the synthesis parameters and compare them with common preparation strategies. This paper reports the main requirements for reproducible and scalable production of CQDs solution, such as the mass of the precursors for each process, for the first time. The conversion and yield percentages on a dry basis, mass of CQDs powder agglomerates, and the specific time duration for each individual step are provided to compensate for the variability of the raw material and facilitate statistical comparison of the methodology. A TEM micrographs analysis for CQDs reconstitution process was conducted, which can be considered a potential mechanism for CQDs agglomeration process.

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity

BackgroundImmunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).MethodsWe applied artificial intelligence (AI)-based imaging analyses to Cell Paint-stained cells exposed to drug libraries, identifying CON as a marker for ICD. CON was characterized using SYTO 14 fluorescent staining and holotomographic microscopy, and visualized by AI-deconvoluted transmitted light microscopy. A neural network-based quantitative structure-activity relationship (QSAR) model was trained to link molecular descriptors of compounds to the CON phenotype, and the classifier was validated using an independent dataset from the NCI-curated mechanistic collection of anticancer agents.ResultsCON strongly correlated with the inhibition of DNA-to-RNA transcription. Cytotoxic drugs that inhibit RNA synthesis without causing DNA damage were as effective as conventional cytotoxicants in inducing ICD, as demonstrated by DAMPs release/exposure and vaccination efficacy in mice. The QSAR classifier successfully predicted drugs with a high likelihood of inducing CON.ConclusionsWe developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.

Effects of different production methods and hybridization on mechanical characteristics of basalt, flax, and jute fiber-reinforced composites

Abstract The advanced material properties and high quality of composite products are largely dependent on the production methods. In the current search, it was examined how the mechanical properties of flax, jute, and basalt fiber-reinforced composites were affected by three different production methods and hybridization processes. The materials were produced by hand lay-up, vacuum bagging, and vacuum-assisted resin transfer molding methods. While the physical and mechanical properties of specimens were determined by hardness, tensile, and water absorption tests, the fiber behavior of the composites was investigated by micrograph analysis. Tensile test data gave better results for homogeneous samples by 1.4–16.4 % for the samples produced with the use of vacuum-assisted resin transfer technique in comparison to the samples manufactured by the vacuum bagging technique, and by 7.3–22.5 % in comparison to the samples fabricated by the hand lay-up technique. Compared to the other two methods, 7.3–35.3 % better tensile strength results were obtained when using the VARTM technique in hybrid samples. Moreover, the hardness value measurement of the samples also supports the tensile strength data. However, the water absorption analysis results display that samples produced by hand lay-up absorb less water than samples produced by the other two production methods.

Evaluation of sodium alginate sponge infused bromelain spray and Helichrysum italicum nanoemulsion to accelerate wound healing.

This research introduces a pioneering porous sponge composed of sodium alginate/gellan gum polymers manufactured via the freeze-drying method. Bromelain was encapsulated in H. italicum nanoemulsion and sprayed using a spray device containing a glass gun with a 0.2μm nozzle. Physicochemical properties, including swelling capacity (1570.48±54.2), porosity (88.860±5.7), biodegradability (98.21±8), shape memory, FTIR, and SEM analyses were performed. Blood absorption (1465±82%), anti-inflammatory, and antibacterial activity against various pathogens (35mm S. aureus, 23mm E. coli, 21mm P. aeruginosa) also were investigated. SA/GG/BR/NEHro sponge showed excellent anti-inflammatory (89.34±4.2) and demonstrated effective antibacterial properties, which can help safeguard the wound against bacterial infection. FTIR analysis correctly confirms the presence of bromelain and oil nanoemulsion and SEM micrograph analysis showed high porosity of sponges containing H. italicum oil nanoemulsion. SA/GG/BR/NEHro exhibited remarkable compressive flexibility, mechanical stability, and shape memory properties. The results also show that bromelain helped reduce inflammation, promote tissue repair, and accelerate wound closure. In vitro and in vivo wound healing studies revealed that the sponges exhibited excellent homeostasis. Notably, the SA/GG/BR/NEHro sponge achieved complete closure of full-thickness wounds (100%), underscoring its exceptional performance in wound repair and regeneration.

Preparation methodology for the microstructural characterization of diffusion layers in a titanium/steel composite

Abstract Hot pressing of titanium and carbon steel leads to the formation of a diffusion layer at the interface. Depending on the carbon content of the steel used, it either exclusively contains TiC or additional other phases. In the case of steel with a medium carbon concentration (0.67 wt.% C), a pure TiC layer forms. A preparation methodology was developed to make statements about functional and microstructural properties of the respective layers such as layer thickness, porosity, or grain size. However, apart from the diffusion layer’s microstructure, it also reveals the microstructure of the two base substrates. A comparison based on electron backscatter diffraction (EBSD) examinations yields similar results in terms of microstructure. A micrograph analysis based on the new preparation methodology also allows confirming the element distribution measurement by glow discharge optical emission spectroscopy (GDOES). The methodology therefore provides a way of quickly and reliably controlling the layer formation during the hot pressing process of titanium and carbon steel.

Cholesterol-driven modulation of membrane-membrane interactions by an antimicrobial peptide, NK-2, in phospholipid vesicles

Antimicrobial peptides (AMPs) are essential components of the innate immune system, demonstrating their antimicrobial effects primarily through the creation of transmembrane pores that result in membrane disruption. Cholesterol within the membrane can significantly affect the interaction between AMPs and the membrane, as it is known to alter both the permeability and elastic properties of the membrane. In this study, we have investigated the influence of cholesterol on the interaction of the AMP, NK-2 with phospholipid vesicles. We prepared giant unilamellar vesicles (GUVs) composed of DOPC-DOPG and Egg PC, varying the cholesterol concentrations, and analyzed them using phase contrast microscopy. The aggregation of vesicles is evident in the phase contrast microscopy observations of GUVs. The aggregation of GUVs with cholesterol ultimately leads to a collapse state, a condition not typically seen in GUVs lacking cholesterol. Furthermore, the aggregation kinetics were determined from the analysis of phase contrast micrographs. This biophysical investigation offers valuable insights into how cholesterol affects the interactions between membranes induced by antimicrobial peptides.

Quantifying the fractal complexity of nutrient transport channels in Escherichia coli biofilms under varying cell shape and growth environment.

Recent mesoscopic characterization of nutrient-transporting channels in Escherichia coli has allowed the identification and measurement of individual channels in whole mature colony biofilms. However, their complexity under different physiological and environmental conditions remains unknown. Analysis of confocal micrographs of colony biofilms formed by cell shape mutants of E. coli shows that channels have high fractal complexity, regardless of cell phenotype or growth medium. In particular, colony biofilms formed by the mutant strain ΔompR, which has a wide-cell phenotype, have a higher fractal dimension when grown on rich medium than when grown on minimal medium, with channel complexity affected by glucose and agar concentrations in the medium. Osmotic stress leads to a dramatic reduction in the ΔompR cell size but has a limited effect on channel morphology. This work shows that fractal image analysis is a powerful tool to quantify the effect of phenotypic mutations and growth environment on the morphological complexity of internal E. coli biofilm structures. If applied to a wider range of mutant strains, this approach could help elucidate the genetic determinants of channel formation in E. coli colony biofilms.

Formulation of Microemulsion-Based Gels for Enhanced Topical Administration of Nonsteroidal Anti-Inflammatory Drugs.

Nonsteroidal anti-inflammatory drugs are commonly administered orally to manage pain and inflammation, but they can have negative gastrointestinal side effects. Topical delivery is an alternative, and microemulsions (μEs) have been shown to be effective in facilitating, but they suffer from a liquid nature and low long-term retention on the skin. Hence, microemulsified gels (μEGs) have been developed, and in this study, we explored certain μEGs with diclofenac sodium (DF-Na) and naproxen sodium (NP-Na) with the hypothesis to ensure a slower and more sustained delivery of NSAIDs through the skin. The μEGs comprised castor oil (∼8%), water (∼12%), Tween-20 (∼72%), Span-20 (∼8%), poloxamer 407, and DF-Na or NP-Na. Optical microscopy was used to study the microstructures in the μEs and μEGs, and phase transitions from water-in-oil (w/o) to oil-in-water (o/w) with continuous networks were observed. Based on studies with dynamic light scattering and analyses of electron micrographs, it was observed that the μEs and μEGs loaded with DF-Na and NP-Na comprised monomodal nanodroplets. The average sizes of the droplets were (∼35 nm) and (∼60 nm) for the μEGs, without and with drugs. Fluorescence spectroscopy was used to ensure that the drugs were more likely to be present in the hydrophobic microenvironment of the formulations. Moreover, ex vivo permeation studies were conducted at pH values of 5.5 and 7.4 across rabbit skin. The release rates of DF-Na (>99 ± 1.5%, P < 0.07) and NP-Na (>89 ± 1.1%, P < 0.01) were slower for the μEGs within 8-10 h than for the μEs at the low pH, which is of relevance to the optimal pH of the skin. It was observed that μEGs with high viscosities are effective and may have potential for use in topical drug delivery applications.

Understanding the Morphology and Cross‐Link Density in Silicone Rubber‐Multiwalled Carbon Nanotube Composites via Solvent Transport Studies

ABSTRACTThe diffusion characteristics of silicone rubber‐based nanocomposites have not been extensively studied in the literature. This study aims to provide a comprehensive analysis of the solvent transport properties of these materials, incorporating detailed dissolution modeling. This study reported a novel approach to elucidate the morphological and diffusion characteristics of silicone rubber‐MWCNT (multiwalled carbon nanotube) composites. FESEM micrograph analysis reveals structural changes with the lower loadings forming continuous networks and higher loadings leading to agglomeration of the fillers. Diffusion studies highlight reduced solvent uptake over time due to compact physical networks, while Kraus plot analysis confirms MWCNTs' reinforcing ability. Dissolution modeling using Korsmeyer–Peppas and Peppas–Sahlin models indicates the type of solvent release behavior, with the latter offering a superior fit. Mode of transport analysis suggests a less Fickian mode influenced by MWCNT loading, while swelling parameters demonstrate hindered solvent transport with increasing MWCNT content. The molecular mass between successive cross‐links and the cross‐link density decreases with rising MWCNT loading, which is theoretically predicted by the affine model. This study also focused on the complex interplay between filler loading, composite structure, and solvent transport behavior in silicone rubber‐MWCNTs composites, offering valuable insights for their potential applications in various fields.

Extracting ductile cast iron microstructure parameters from fracture surfaces: A deep learning based instance segmentation approach

This study investigates the deep-learning based microstructural analysis from SEM images of ductile cast iron fracture surfaces. A Mask R-CNN model was trained, achieving 70% precision and 75% recall in graphite particle detection. Combined with a fracture surface reconstruction using the.4-quadrant backscattered electron signal, key parameters, including the particle size, shape and distance were extracted accurately. Compared to micrograph analysis, following probabilistic simulations showed the impact of the higher microstructural variance for the fracture surfaces on crack initiation, leading to higher scatter and elevated crack resistance curves. This highlights the potential of deep-learning based analysis for comprehensive microstructural characterization.

Rubbing-induced site-selective deposition of 2D material patterns on nanomembranes

2D-layered materials (e.g., graphene and transition metal dichalcogenides) have attracted huge attention due to their unique mechanical and electrical properties. Emerging research efforts, which seek to combine device characterization and high-resolution electron micrography analysis for 2D-layered device features, demand nano/microlithographic techniques capable of producing ordered 2D material patterns on ultrathin membranes with nanoscale thicknesses. However, such membranes are so fragile that most conventional lithographic techniques can be hardly performed on them to generate 2D material patterns. Our previous works have demonstrated that the rubbing-induced site-selective (RISS) deposition method can produce arbitrary 2D semiconductor (e.g., MoS2 and Bi2Se3) patterns on regular device substrates. This fabrication route prevents the vulnerable 2D-layered structures from the detrimental damage introduced by plasma etching and resist-based lithography processes. In this work, we explore the applicability of RISS for directly producing 2D material patterns on nanomembranes. Specifically, this work shows that a polymeric interfacing layer on the rubbing template features, which can effectively prevent stress concentration during the rubbing process, is crucial to successful implementation of RISS processes on nanomembranes. Furthermore, we carried out the mechanics simulation of the Von Mises stress and pressure distribution on the RISS-processed membrane to identify the optimal rubbing load, which can generate sufficient triboelectric charge for material deposition but no damage to the membrane. Using this approach, we have successfully demonstrated the deposition of Bi2Se3 patterns on 25 nm SiOx nanomembranes and high-resolution transmission electron micrography characterization of the crystallographic structures.

CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

The influence of 3D printing mode on the chemical composition and structure of 30HGSA steel

The authors carried out the study of the influence of 3D printing modes on the structure and chemical composition of 30HGSA steel (chromansil) samples produced by the method of additive electric arc surfacing. To study the influence of the electric arc surfacing mode on the chemical composition of the steel under study, an optical emission analysis of the samples was carried out. The influence of the surfacing mode on the resulting structure was assessed over the entire height of the deposited walls at magnifications of ×50, ×100, ×200 and ×500. Optical emission analysis identified a change in the material chemical composition associated with the loss of chemical elements. It was found that the degree of loss of C, Cr and Si increases almost linearly and is directly proportional to the surfacing heat input (Q, J/mm). The exact influence of an increase in the surfacing heat input on the Mn content was not found, but a relationship between the degree of its loss and the voltage (U, V) during surfacing of samples was identified. Microstructural studies of all samples did not reveal a large number of systemically formed structural defects characteristic of cast and welded products (pores, shrinkage cavities, etc.), which confirms the high quality of the metal in goods produced by electric arc surfacing. Analysis of micrographs taken in different areas of the samples allowed determining that the metal microstructure does not undergo significant changes under different surfacing modes; the main tendencies in changes in the structure along the height of the sample are preserved. All samples demonstrated the formation of a highly dispersed structure, regardless of the 3D printing parameters. The most favorable metal structure, suitable for subsequent use in the production of goods using additive manufacturing, was recognized as the structure of the sample deposited using mode No.5 (I=160A, U=24V, Q=921.6J/mm). This mode can be used for further study of the problems of additive electric arc surfacing of 30HGSA steel.

Remodeling of the structural components of the capsule and glomerular zone of the adrenal glands cortex of white rats under the influence of a complex of food additives at the late terms of the experimental study

With food additives, we can add flavour to food, extend its shelf life, and make it look good. However, even small amounts of these additives can lead to diseases of various organs and systems. The aim of our work is to establish metrics and morphological changes in the structure of the capsule and the glomerular zone of the cortex of the adrenal glands of rats at the later stages of the experimental study under the conditions of consumption of a complex of food additives: monosodium glutamate, sodium nitrite and Ponceau 4R. The control group of rats consumed oral drinking water and saline. The rats of the experimental groups were orally administered once a day with a 10 % solution of sodium nitrite (E250), sodium glutamate (E621) or Ponceau 4R. The dosages of food additives were two times lower than the permissible norm in food. The rats were withdrawn from the experiment after 8, 12 and 16 weeks. The adrenal glands were removed for the study. Paraffin sections were stained with hematoxylin and eosin, and semi-thin sections with toluidine blue, and examined using a light microscope Biorex with a digital microphoto attachment DSM 900. Ultrathin sections were examined in a TEM-125 K electron microscope. For morphometric analysis, the structure of the capsule and the glomerular zone of the cortex of the adrenal glands of rats at the later stages of the experimental study was studied. The study results were analysed using the software package “InStat”. At the late stages of the experimental study, the adrenal capsule underwent irreversible changes. The average thickness of the adrenal connective tissue capsule decreased by the end of the 16th week of the experimental research. Similarly, the average thickness of the cortex decreased with each subsequent week relative to the control values, indicating significant structural and functional disorders. The average thickness of the glomerular zone of the adrenal cortex periodically changed as a compensatory reaction in response to the administration of a complex of food additives to rats. The phenomena of nucleus pyknosis and sludge syndrome were detected, which indicated compensatory and adaptive processes at the microscopic level. The analysis of electron micrographs revealed destroyed granular endoplasmic reticulum cisternae and mitochondrial remnants. Thus, it has been established that using a complex of food additives (monosodium glutamate, sodium nitrite and Ponceau 4R) directly affects the adrenal glands of rats. This effect causes structural changes in the cortex and medulla, causing alteration and hypertrophy of cells in these areas. Additionally, hyperhydration of the connective tissue stroma and compensatory-restorative reactions aimed at transcription factors and internal mechanisms of nitric oxide formation were recorded.

Experimental review and accuracy of etchants used for phase analysis of SAF2507 Super Duplex Stainless Steel

The mechanical and corrosion properties of duplex stainless steels (DSS) depend on the distribution of ferrite and austenite as well as the presence of secondary phases. It is therefore necessary to accurately determine both the distribution of the phases and their morphology. The aim of this research is to define a standard method for the determination of the volume fraction of the phases present in a SAF2507 SDSS by analysing images acquired by light microscope. To do this, the material is heat-treated at 850 °C for 5 min, 15 min, 30 min, 1h, 5h to stimulate the formation of the σ-phase. The metallographic etchant capable of maximising phase contrast is then identified. Micrographic analyses are performed using different reagents at different holding times and temperatures, in order to find the best combination. A standard procedure is then defined for the image analysis. The data obtained were are processed to calculate the phase distribution. The results are compared with the quantitative analysis performed using X-ray diffraction as reference.The research evidence that Kalling's reagent is best etchant for fully solubilised materials, as it accurately estimates ferrite and austenite content. On the other hand, Marble's reagent should be preferred for the detection of σ-phase. For comprehensive analysis of all phases, Murakami's reagent is recommended.The approach defined in this paper not only improves the understanding of the microstructure of DSS, but also provides an important tool for industrial quality control and material characterisation.

Centrifuge modeling of levees with geocomposite chimney drain subjected to flooding

The objective of present study is to evaluate the performance of a levee subjected to flooding and seepage through centrifuge model tests. For this, six model tests were conducted on a 240 mm high levee model at 30g in a 4.5 m radius large beam centrifuge. A custom-developed simulator is employed to induce identical flood rates on the upstream side of models. Further, using (a) geocomposite (GC) and (b) sand-sandwiched geocomposite (SSGC) as internal chimney drain, the suitability of GC material for dissipation of pore-water pressure (PWP) is also studied. The results of the centrifuge tests are discussed in terms of the development of upstream flood function, PWP within the levee body, and the surface settlements at the levee's crest. Further, the influence of an internal chimney drain, the material used for its construction, and its type and composition on the seepage response of the levee are discussed. It is observed that a GC-based chimney drain with sand cushioning on both sides in the horizontal portion of the chimney drain performs well. Further, digital image analysis of SEM micrographs of exhumed GC after centrifuge tests and the analyzed PWP data during sustained flooding-induced seepage is found to corroborate well.

Tribology of Mg-B4C nanocomposites fabricated following powder metallurgy route

In this experimental work, magnesium nanocomposite reinforced with various percentage of boron carbide (0.5%, 1.0%, 1.5%, 2.0% by volume) is fabricated using conventional powder metallurgy technique. Characterisation and wear test of the fabricated composite have been performed using scanning electron microscopy, tribology test monitor respectively. It is observed that addition of B4C increases the hardness and wear resistance linearly. Addition of 2% B4C produces the hardness of the nanocomposites as high as 57.93 ± 4.9 Hv. Furthermore, the nanocomposites displayed improved wear resistance and lower friction coefficient compared to the base magnesium. The influence of sliding speed on these properties has been systematically presented. The paper provides an in-depth exploration of wear mechanisms through a meticulous analysis of SEM micrographs of worn surfaces. This work offers valuable insights into the microstructural characteristics and tribological behaviour of Mg-B4C nanocomposites. These findings underscore the potential application of Mg-B4C nanocomposites in the development of lightweight wear-resistant materials. The impact of incorporating minute amounts of nano-sized boron carbide into Mg, on its behaviour under small loads and repeated scratching has been investigated in this study. Moreover, introspection of the wear mechanisms and the effectiveness of the powder metallurgy technique in fabricating composite materials is presented.